Many entities use systems that rely on integrated and/or add-on analytical tools to analyze, summarize, or otherwise process large amounts of data. For example, a typical data system may include databases containing large stores of data on which a data analysis engine can execute various analytical operations. Resulting analytical data can be compiled into an analysis report according to a particular report definition. The report definition may specify any number of analytical operations to produce analytical data with specific dimensions that are presented in a specific analysis report format (e.g., level of granularity, time period, specific product groups/types presented in a tabular form).
Although a resulting analysis report may be useful to specific end users (e.g., business users), a typical user is not usually capable or interested in defining the report definition (i.e., designing and programming the necessary backend analytical operations). Indeed, most end users do not normally have the level of technical expertise necessary to easily understand resulting reports, let alone define and implement analytical operations to create their own report definitions.
To increase the usability of analysis reports and/or results, various techniques have been developed to present the analytical data to non-technical users in more easily understandable formats. For example, some systems generate analytical data that can be represented in one or more visual analytics (e.g., charts, graphs, tables, etc.) that are easily understandable by non-technical users. Based on individual needs, non-technical users can select the predetermined visual analytics to be included in customized or personalized user interface (UI) portals commonly referred to as “dashboards”. Such dashboards often include graphical user interfaces (GUIs) that present users with selections (e.g. a set) of compact and succinct representations or visualizations of the visual analytics within an organized framework (e.g., a grid of framed visual analytics having to do with specific aspects of a data) that are of particular importance or relevance.